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Copyright EM LYON Par accord du CFC Cession et reproduction interdites Research in Entrepreneurship- The problem of unobserved heterogeneity Frédéric Delmar.

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Presentation on theme: "Copyright EM LYON Par accord du CFC Cession et reproduction interdites Research in Entrepreneurship- The problem of unobserved heterogeneity Frédéric Delmar."— Presentation transcript:

1 Copyright EM LYON Par accord du CFC Cession et reproduction interdites Research in Entrepreneurship- The problem of unobserved heterogeneity Frédéric Delmar Professor, PhD EMLYON Business School, France And Research Institute of Industrial Economics, Sweden

2 I Agenda 1.Who am I? 2.Unobserved heterogeneity and entrepreneurship Related subject and example working paper 3.Delmar & Wiklund (2008) The effect of small business managers’ growth motivation on firm growth. ETP, Spring: 437- 457 4.Delmar & Shane (2006) Does experience matter? Strategic Organization, vol 4(3): 215-247 Easy to understand subject, lot of research already, however a lot of reminding problems in theory and method

3 II Who am I? Editorial board: JBV, ET&P, SO! Occasional reviewer: SMJ, AMJ, MSc, SBE, and others In 16 years of research: –Two A (SMJ and MSc) –Nine B (JBV, ET&P, ERD, SO!) –Five C and about 20 book chapters Subjects: behavior, growth, firm establishment Center for Research in New Venture Creation and Growth, EMLYON Business School

4 IV Unobserved heterogeneity and Management Heterogeneity=> –Unobserved heterogeneity=> omitted variable problem Observational data=> –Data is interpreted => Theory describes what is the universe of relevant variables to be included to test hypotheses The role of theory: –Counterfactual causality reasoning in theory allows us to identify relevant variables and how to test them Solutions both in cross-sectional and longitudinal data to handle the problem with omitted variables

5 Definition or why it is a problem Heterogeneity and causality: –The existence of an unobserved variable (u) that may affect the interpretation of variable X on Y. –Hence the estimation (coefficient) of X on Y is biased if u is not measured (coefficient and standard error). –Further more we cannot estimate the probability how u will affect object i. That is u is not randomly distributed. –For example, the value of the entrepreneurial opportunity for any entrepreneurial process, or motivation of individual in matched data

6 Here is a question for you You are asked to conduct an observational study to estimate the effect of business plan or experience on the risk of firm termination or firm performance. How would you design the study and what assumptions are you making? –Option 1: a large set of heterogeneous sample of firm (different industries, at different locations, different opportunities …) –Option 2: a smaller more homogenous sample of firms (all on the same opportunity) Would you use a cross-sectional or longitudinal data set? Feel free to change to change business plan for helmet, firm dissolution for death on a motorcycle

7 Here are some possible choices or assumptions you are making Theory: –What is really relevant to measure according to theory, what other interpretations can be made, how can I exclude them? –Can I construct a counterfactual argument? –Am I primarily interested in between process or within process variance Design: –I can control away a lot of the effects by including other variables –I can used randomization –I can assume that unobserved is too little to pose a problem Analysis: –Cross sectional –Longitudinal

8 Theoretical issues 1.The formal establishing of causality is a property of a model not of data or statistical analyses. 2.Many models can explain the same data. 3.Assumptions must be made to identify causal models. –Hence, a good theory allows us to derive a model which represents a logically consistent system within which hypothetical “thoughts experiments can be conducted to examine the effects of changes in parameters and constraints on outcomes” (Heckman, 2000, p. 46). Entrepreneurship as heterogeneity and process

9 Counterfactual argument “if it had been the case that c (or not C), it would have been the case that E (or not E)” Important for observational data The researcher need to be clear on what the specific cause is, why it should affect the particular outcome and what would happen in the absence of the cause being present. The later is the counterfactual argument. The counterfactual argument is only as compelling as the logic and “evidence” offered by the researcher to verify the links between the hypothesized antecedent and its expected consequences Finding similar cases!

10 Design issues The use of randomization is not possible (unethical or infeasible) Control variables consume degrees of freedom, and u might be truly unobservable Are we more concerned with internal validity or generalizability? –Smaller, more homogenous sample reduces both sampling variability and sensitivity to unobserved bias –Increasing the sample reduces sampling variability, but not unobserved bias Longitudinal designs

11 Analysis issues Cross-sectional and longitudinal –Moderator and mediator –Instrument variables –Sensitivity analysis Longitudinal: –Random effects vs. fixed effects Unobservable is time invariant; between and within variance –Matching or propensity Score methodology

12 Conclusions Unobserved heterogeneity, biased estimators and problems with inference of causality The role of theory in observational data Design=>clever designs Analysis –Think carefully on the assumptions made –Sensitivity analysis How about wearing business plan or experience?

13 Having experience or a business plan Different outcomes due to different: –Different economic models, different periods, different learning abilities, motivation, resources, network, opportunities, quality of information Ho to do it? –Firms started at the same time in the same context using a matching procedure.

14 Wearing helmet Different crashes due to different: – motorcycles, speed, forces, roads, types of traffics, obstacles. How to do it? –Two people riding the same motorcycle, one with helmet and one without The effect? –40% reduction in risk associated with helmet use

15 Conclusion The role of causality in research The problem and specific challenges of observational data Theory and the counterfactual argument Design – homogenous sample for greater internal validity Analysis- Many available techniques (fixed and random effects, matching)

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